import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
#xml转yolo格式txt


#建立列表，自己的数据集，改为自己的即可
classes=["ball","messi"]
DATA_DIR_NAME= "VOCdevkit" #存放所有数据集的位置
WORK_DIR_NAME= "VOC2007" #某个数据集文件夹
IMAGE_DIR_NAME= "JPEGImages"
ANNOTATION_DIR_NAME= "Annotations"
YOLO_LABELS_DIR= "YOLOLabels" #在work文件夹下生成txts
#将文件分为train和val转化为txt并生成的目录在data_dir_name下
TRAIN_DIR_NAME= "train"
VAL_DIR_NAME= "val"
YOLOV5_IMAGES_DIR_NAME= "images" #分为train和val两个文件夹的路径
YOLOV5_LABELS_DIR_NAME= "labels"

xml_path=os.path.join(DATA_DIR_NAME, WORK_DIR_NAME, ANNOTATION_DIR_NAME, "%s.xml")
out_txt_path=os.path.join(DATA_DIR_NAME, WORK_DIR_NAME, YOLO_LABELS_DIR, "%s.txt")

#清除隐藏文件
def clear_hidden_files(path):
    dir_list = os.listdir(path)
    for i in dir_list:
        abspath = os.path.join(os.path.abspath(path), i)
        if os.path.isfile(abspath):
            if i.startswith("._"):
                os.remove(abspath)
        else:
            clear_hidden_files(abspath)
#把pascolvol数据格式转化成yolo格式
def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)
#把每一个xml都转化为yolo格式的txt
def convert_annotation(image_id):
    in_file = open(xml_path %image_id)
    out_file = open(out_txt_path %image_id, 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    in_file.close()
    out_file.close()

#作用：保证文件夹都存在+清除隐藏文件夹
#必须要放在同一路径
wd = os.getcwd()
#数据集目录
data_base_dir = os.path.join(wd, DATA_DIR_NAME)
if not os.path.isdir(data_base_dir):
    os.mkdir(data_base_dir)
#数据目录
work_sapce_dir = os.path.join(data_base_dir, WORK_DIR_NAME)
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
#标注目录
annotation_dir = os.path.join(work_sapce_dir, ANNOTATION_DIR_NAME)
if not os.path.isdir(annotation_dir):
        os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
#文件目录
image_dir = os.path.join(work_sapce_dir, IMAGE_DIR_NAME)
if not os.path.isdir(image_dir):
        os.mkdir(image_dir)
clear_hidden_files(image_dir)
YOLO_LABELS_DIR = os.path.join(work_sapce_dir, YOLO_LABELS_DIR)
if not os.path.isdir(YOLO_LABELS_DIR):
        os.mkdir(YOLO_LABELS_DIR)
clear_hidden_files(YOLO_LABELS_DIR)
yolov5_images_dir = os.path.join(data_base_dir, YOLOV5_IMAGES_DIR_NAME)
if not os.path.isdir(yolov5_images_dir):
        os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, YOLOV5_LABELS_DIR_NAME)
if not os.path.isdir(yolov5_labels_dir):
        os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, TRAIN_DIR_NAME)
if not os.path.isdir(yolov5_images_train_dir):
        os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, VAL_DIR_NAME)
if not os.path.isdir(yolov5_images_test_dir):
        os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, TRAIN_DIR_NAME)
if not os.path.isdir(yolov5_labels_train_dir):
        os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, VAL_DIR_NAME)
if not os.path.isdir(yolov5_labels_test_dir):
        os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)

#这个文件是包含了文件的路径
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
#'D:\\biancheng_heji\\learn-python-numpy-tensor\\src\\learn-yolo\\网课YOLOv5实战Windows\\VOCdevkit\\VOC2007\\JPEGImages\\'
# probo = random.randint(1, 100) #1-100的随机数
# print("Probobility: %d" % probo)
# 对所有的图片进行遍历
for i in range(0,len(list_imgs)):
    path = os.path.join(image_dir,list_imgs[i])
    if os.path.isfile(path):
        image_path = os.path.join(image_dir , list_imgs[i])
        voc_path = list_imgs[i]
        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
        (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
        annotation_name = nameWithoutExtention + '.xml'
        annotation_path = os.path.join(annotation_dir, annotation_name)
        label_name = nameWithoutExtention + '.txt'
        label_path = os.path.join(YOLO_LABELS_DIR, label_name)
    probo = random.randint(1, 100)
    print("Probobility: %d" % probo)
    if(probo < 80): # train dataset 小于80划分到数据集
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, os.path.join(yolov5_images_train_dir , voc_path))
            copyfile(label_path, os.path.join(yolov5_labels_train_dir , label_name))
    else: # test dataset    大于80划分到验证集
        if os.path.exists(annotation_path):
            test_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, os.path.join(yolov5_images_test_dir , voc_path))
            copyfile(label_path, os.path.join(yolov5_labels_test_dir , label_name))
train_file.close()
test_file.close()
